Lynch Lab members Casey Youngflesh and Catherine Foley (in addition to some folks at TACC) kicked off this year’s POLAR 2018 conference by teaching a 2-day Software Carpentry/High Performance Computing workshop. The workshop was made possible by an NSF Research Coordination Grant, which endeavors to develop better cyberinfrastructure resources for complex analyses conducted by polar scientists. Thanks to everyone for attending! All workshop materials can be found here.

# Congratulations to Casey Youngflesh on the American Ornithological Society Presentation Prize

Lab graduate student Casey Youngflesh was awarded the American Ornithological Society Presentation Prize for his presentation at the World Seabird Twitter Conference 4. The conference challenged participants to communicate their research in four tweets, using anything from hashtags to gifs. Check out all the conference presentations (including Michael Schrimpf’s) on twitter using the #WSTC4 hashtag. Follow Casey on twitter @caseyyoungflesh

# Lynch Lab undergraduate researchers at research symposium

Last week undergraduate researchers Iftikar Ahmed, Sara Vincent, and Abigail Higgins presented at the Stony Brook University URECA undergraduate research symposium. Some great research on display on a variety of topics!

# `MCMCvis` package peer reviewed and published!

`MCMCvis`

, created by lab PhD candidate Casey Youngflesh is now peer reviewed and published in the *Journal of Open Source Software*! JOSS is journal that focuses exclusively on scientific software. While I think most would agree that giving software developers credit for their work is important, I think the practice of citing R packages (at least in ecology) is relatively rare. I (Casey) myself am guilty of this, to be sure. Cite the R packages/other software you use – publications and citations of those pubs are the currency of academia!

# Sea Ice from Space: Crossing Boundaries of Sea Ice Science

Lab PhD candidate, Catherine Foley has a great piece out in Earthzine this week about the importance of sea ice to polar ecosystems and how scientists use satellites to study it. Check it out!

# Reid Biondo at Long Island Science and Engineering Fair

Lynch Lab High School student, Reid Biondo’s work was highlighted in The Port Times Record this week! Reid was working on using Convolutional Neural Networks to classify Antarctic Seals.

# Check your prior posterior overlap (PPO) – MCMC wrangling in R made easy with `MCMCvis`

**R-hat (AKA Gelman-Rubin statistic)**– used to assess convergence of chains in the model**Visual assessment of chains**– used to assess whether posterior chains mixed well (convergence)**Visual assessment of posterior distribution shape**– used to determine if the posterior distribution is constrained**Posterior predictive check (predicting data using estimated parameters)**– used to make sure that the model can generate the data used in the model

## PPO

One check, however, is often missing: **a robust assessment of the degree to which the prior is informing the posterior distribution**. Substantial influence of the prior on the posterior may not be apparent through the use of R-hat and visual checks alone. Version 0.9.2 of `MCMCvis`

(now available on CRAN), makes quantifying and plotting the prior posterior overlap (PPO) simple.

`MCMCvis`

is an R package designed to streamline analysis of Bayesian model results derived from MCMC samplers (e.g., JAGS, BUGS, Stan). It can be used to easily visualize, manipulate, and summarize MCMC output. The newest version is full of new features – a full tutorial can be found here.

## An example

To check PPO for a model, we will use the function `MCMCtrace`

. As the function is used to generate trace and density plots, checking for PPO is barely more work than just doing the routine checks that one would ordinarily perform. The function plots trace plots on the left and density plots for both the posterior (black) and prior (red) distributions on the right. The function calculates the percent overlap between the prior and posterior and prints this value on the plot. See `?MCMCtrace`

in R for details regarding the syntax.

```
#install package
install.packages('MCMCvis', repos = "http://cran.case.edu")
#load package
require(MCMCvis)
#load example data
data(MCMC_data)
#simulate data from the prior used in your model
#number of iterations should equal the number of draws times the number of chains (although the function will adjust if the correct number of iterations is not specified)
#in JAGS: parameter ~ dnorm(0, 0.001)
PR <- rnorm(15000, 0, 32)
#run the function for just beta parameters
MCMCtrace(MCMC_data, params = 'beta', priors = PR, pdf = FALSE)
```

## Why check?

Checking the PPO has particular utility when trying to determine if the parameters in your model are identifiable. If substantial PPO exists, the prior may simply be dictating the posterior distribution – the data may have little influence on the results. If a small degree of PPO exists, the data was informative enough to overcome the influence of the prior. In the field of ecology, nonidentifiability is a particular concern in some types of mark-recapture models. Gimenez (2009) developed quantitative guidelines to determine when parameters are robustly identifiable using PPO.

While a large degree of PPO is not always a bad thing (e.g., substantial prior knowledge about the system may result in very informative priors used in the model), it is important to know where data was and was not informative for parameter estimation. The degree of PPO that is acceptable for a particular model will depend on a great number of factors, and may be somewhat subjective (but see Gimenez [2009] for a less subjective case). Like other checks, PPO is just one of many tools to be used for model assessment. Finding substantial PPO when unexpected may suggest that further model manipulation is needed. Happy model building!

## Other `MCMCvis`

improvements

Check out the rest of the new package freatures, including the option to calculate the number of effective samples for each parameter, ability to take arguments in the form of a â€˜regular expressionâ€™ for the `params`

argument, ability to retain the structure of all parameters in model output (e.g., parameters specified as matrices in the model are summarized as matrices).